The training problem for feedforward neural networks is nonlinear parameter estimation that can be solved by a variety of optimization techniques. Much of the literature on neural networks has focused on variants of gradient descent. The training of neural networks using such techniques is known to be a slow process with more sophisticated techniques not always performing signiicantly better. In this paper, we show that feedforward neural networks can have ill-conditioned Hessians and that this… CONTINUE READING